Bidirectional LSTM network for speech emotion recognition.
Bidirectional LSTM network for speech emotion recognition.
Environment:
Long option | Option | Description |
---|---|---|
--dataset | -d | dataset type |
--dataset_path | -p | dataset or the predicted data path |
--load_data | -l | load dataset and dump the data stream to a .p file |
--feature_extract | -e | extract features from data and dump to a .p file |
--model_path | -m | the model path you want to load |
--nb_classes | -c | the number of classes of your data |
--speaker_indipendence | -s | cross validation is made using different actors for train and test sets |
Example find_best_model.py:
python find_best_model.py -d "berlin" -p [berlin data path] -l -e -c 7
Example prediction.py:
python prediction.py -p [data path] -m [model path] -c 7
Example model_cross_validation.py:
python model_cross_validation.py -d "berlin" -p [berlin data path] -l -e -c 7
S. Mirsamadi, E. Barsoum, and C. Zhang, “Automatic speech emotion recognition using recurrent neural networks with local attention,” in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, U.S.A., Mar. 2017, IEEE, pp. 2227–2231.
Fei Tao, Gang Liu, “Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition,” Submitted to 2018 IEEE International Conference on Acoustics, Speech and Signal Processing.
Video from Microsoft Research